Impact of Glucose Fluctuation on Acute Cerebral Infarction in Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: This study aimed to describe the frequency and temporal profile of acute cerebral infarction (ACI) using a continuous glucose monitoring system (CGMS) in patients with and without type 2 diabetes mellitus (T2DM) and explore the impact of blood glucose fluctuations on the short-term prognosis of ACI. METHODS: The subjects were divided into four groups: T2DM with acute cerebral infarction (DMCI, Group A, n=56); T2DM without acute cerebral infarction (DM-NCI, Group B, n=36); Acute cerebral infarction patients without T2DM (NDM-CI, Group C, n=54); Healthy control group (NG, Group D, n=36). The National Institutes of Health Stoke Scale (NIHSS) and modified Rankin scale (mRs) were collected in Group A and C. All subjects were monitored for 72 hours using the CGMS. Indices such as fasting blood glucose (FBG) and mean amplitude of glycemic excursions (MAGE) were calculated. Glycemic excursions were compared between Group A, B, C and Group D, respectively. Multiple linear regression analysis and logistic analysis was applied. RESULTS: MAGE is related to NIHSS, homocysteine (HCY), HOMA-IR, FBG, CRP and IMT, while NIHSS is related to CRP, HCY, HOMA-IR, IMT. The factors impacting the short-term prognosis of ACI were NIHSS, HBA1C and MAGE. CONCLUSION: Larger glucose fluctuations are associated with more stroke risk factors and are associated with a poorer short-term prognosis. More attention should be paid to glucose fluctuations in patients with ACI and a history of T2DM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".